Colorectal polyps are critical indicators of colorectal cancer (CRC). Classification of polyps during colonoscopy is still a challenge for which many medical experts have come up with visual models, albeit with limited success. An early detection of CRC prevents further complications in the colon, which makes identification of abnormal tissue a crucial step during routinary colonoscopy. In this paper, a classification approach is proposed to differentiate between benign and pre-malignant polyps using features learned from a Triplet Network architecture. The study includes a total of 154 patients, with 203 different polyps. For each polyp an image is acquired with White Light (WL), and additionally with two recent endoscopic modalities:Blue Laser Imaging (BLI) and Linked Color Imaging (LCI). The network is trained with the associated triplet loss, allowing the learning of non-linear features, which prove to be a highly discriminative embedding, leading to excellent results with simple linear classifiers. Additionally, the acquisition of multiple polyps with WL, BLI and LCI, enables the combination of the posterior probabilities, yielding a more robust classification result. Threefold cross-validation is employed as validation method and accuracy, sensitivity, specificity and area under the curve (AUC) are computed as evaluation metrics. While our approach achieves a similar classification performance compared to state-of-the-art methods, it has a much lower inference time (from hours to seconds, on a single GPU). The increased robustness and much faster execution facilitates future advances towards patient safety and may avoid time-consuming and costly histhological assessment.